CVAE
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Related Articles from SNS
Exploring Low Energy Excess in MINER with sapphire detectors using Convolutional Variational Autoencoder (CVAE)
arXiv:2605.31190v1 Announce Type: new Abstract: As cryogenic detectors push toward ever-lower energy thresholds, their sensitivity is increasingly constrained by a persistent low-energy background known as the low-energy excess (LEE). We report observation of LEE in the MINER experiment using a sapphire ($\mathrm{Al_2O_3}$) detector at energies around 200 eV, with the excess reproducibly reappearing after each non-operational warm-up period. To address this limiting background, we implement...
Sem-NaVAE: Semantically-Guided Outdoor Mapless Navigation via Generative Trajectory Priors
Announce Type: replace Abstract: This work presents a mapless navigation approach for outdoor applications. It combines the exploratory capacity of conditional variational autoencoders (CVAEs) to generate trajectories and the semantic segmentation capabilities of a lightweight visual language model (VLM) to select the trajectory to execute. Open-vocabulary segmentation is used to score and select the generated trajectories based on natural language, and a state-of-the-art local planner...
Latent Diffusion Policy: Shaping Latent Spaces for Diffusion-Based Robotic Manipulation
Announce Type: new Abstract: Diffusion-based visuomotor policies operating directly in raw action spaces conflate scene comprehension with trajectory generation within a single denoising process. The resulting velocity field must simultaneously encode scene information and generate precise trajectories, increasing learning complexity and limiting performance on tasks demanding precise temporal coordination across multiple arms. To simplify this joint learning problem, we introduce Latent...
T-GMP: Terrain-conditioned Generative Motion Priors for Versatile and Natural Humanoid Locomotion
arXiv:2606.06944v1 Announce Type: new Abstract: Achieving both anthropomorphic naturalness and robust terrain traversal remains a fundamental challenge in humanoid locomotion. Existing Reinforcement Learning (RL) approaches typically rely on fixed motion priors, limiting their adaptability to varying environments. We propose Terrain-conditioned Generative Motion Priors (T-GMP), a module that captures a terrain-conditioned latent motion manifold from a few expert state-terrain demonstrations...